11 citations · 16 across the 10 of their papers we have counts for
12 papers
C-Mining: Unsupervised Discovery of Seeds for Cultural Data Synthesis via Geometric Misalignment
Pufan Zeng, Yilun Liu, Mingchen Dai +12
Achieving cultural alignment in Large Language Models (LLMs) increasingly depends on synthetic data generation. For such synthesis, the most vital initial step is seed curation; ho…
RationAnomaly: Log Anomaly Detection with Rationality via Chain-of-Thought and Reinforcement Learning
Song Xu, Yilun Liu, Minggui He +10
Logs constitute a form of evidence signaling the operational status of software systems. Automated log anomaly detection is crucial for ensuring the reliability of modern software…
R-Log: Incentivizing Log Analysis Capability in LLMs via Reasoning-based Reinforcement Learning
Yilun Liu, Ziang Chen, Song Xu +10
The growing complexity of log data in modern software systems has prompted the use of Large Language Models (LLMs) for automated log analysis. Current approaches typically rely on…
M-DaQ: Retrieving Samples with Multilingual Diversity and Quality for Instruction Fine-Tuning Datasets
Chunguang Zhao, Yilun Liu, Pufan Zeng +10
Multilingual instruction fine-tuning (IFT) empowers large language models to generalize across diverse linguistic and cultural contexts; however, high-quality, systematically curat…
ELSPR: Evaluator LLM Training Data Self-Purification on Non-Transitive Preferences via Tournament Graph Reconstruction
Yan Yu, Yilun Liu, Minggui He +9
Pairwise evaluation of large language models (LLMs) has become the dominant paradigm for benchmarking open-ended tasks, yet non-transitive preferences, where evaluators prefer A ov…
MIDB: Multilingual Instruction Data Booster for Enhancing Cultural Equality in Multilingual Instruction Synthesis
Yilun Liu, Chunguang Zhao, Xinhua Yang +9
Despite doubts on data quality, instruction synthesis has been widely applied into instruction tuning (IT) of LLMs as an economic and rapid alternative. Recent endeavors focus on i…
R1-T1: Fully Incentivizing Translation Capability in LLMs via Reasoning Learning
Minggui He, Yilun Liu, Shimin Tao +10
Despite recent breakthroughs in reasoning-enhanced large language models (LLMs) like DeepSeek-R1, incorporating inference-time reasoning into machine translation (MT), where human…
Adapting Large Language Models to Log Analysis with Interpretable Domain Knowledge
Yuhe Ji, Yilun Liu, Feiyu Yao +10
Log analysis represents a critical sub-domain within AI applications that facilitates automatic approaches to fault and error management of large-scaled software systems, saving la…
LogLM: From Task-based to Instruction-based Automated Log Analysis
Yilun Liu, Yuhe Ji, Shimin Tao +7
Automatic log analysis is essential for the efficient Operation and Maintenance (O&M) of software systems, providing critical insights into system behaviors. However, existing appr…
LogEval: A Comprehensive Benchmark Suite for Large Language Models In Log Analysis
Tianyu Cui, Shiyu Ma, Ziang Chen +10
Log analysis is crucial for ensuring the orderly and stable operation of information systems, particularly in the field of Artificial Intelligence for IT Operations (AIOps). Large…
From Handcrafted Features to LLMs: A Brief Survey for Machine Translation Quality Estimation
Haofei Zhao, Yilun Liu, Shimin Tao +6
Machine Translation Quality Estimation (MTQE) is the task of estimating the quality of machine-translated text in real time without the need for reference translations, which is of…
Clustering and Ranking: Diversity-preserved Instruction Selection through Expert-aligned Quality Estimation
Yuan Ge, Yilun Liu, Chi Hu +10
With contributions from the open-source community, a vast amount of instruction tuning (IT) data has emerged. Given the significant resource allocation required for training and ev…